Transfer Learning with Optimal Transportation and Frequency Mixup for EEG-based Motor Imagery Recognition

Peiyin Chen, He Wang, Xinlin Sun, Haoyu Li, Celso Grebogi, Zhongke Gao

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)
5 Downloads (Pure)

Abstract

Electroencephalography-based Brain Computer Interfaces (BCIs) invariably have a degenerate performance due to the considerable individual variability. To address this problem, we develop a novel domain adaptation method with optimal transport and frequency mixup for cross-subject transfer learning in motor imagery BCIs. Specifically, the preprocessed EEG signals from source and target domain are mapped into latent space with an embedding module, where the representation distributions and label distributions across domains have a large discrepancy. We assume that there exists a non-linear coupling matrix between both domains, which can be utilized to estimate the distance of joint distributions for different domains. Depending on the optimal transport, the Wasserstein distance between source and target domains is minimized, yielding the alignment of joint distributions. Moreover, a new mixup strategy is also introduced to generalize the model, where the inputs trials are mixed in frequency domain rather than in raw space. The extensive experiments on three evaluation benchmarks are conducted to validate the proposed framework. All the results demonstrate that our method achieves a superior performance than previous state-of-the-art domain adaptation approaches.
Original languageEnglish
Pages (from-to)2866-2875
Number of pages10
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume30
Early online date4 Oct 2022
DOIs
Publication statusPublished - Oct 2022

Keywords

  • electroencephalogram (EEG)
  • brain-computer interface (BCI)
  • transfer learning
  • optimal transportation

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